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THE NATURAL SELECTION: BEHAVIOR ANALYSIS AMONG THE NATURAL SCIENCES

THE NATURAL SELECTION: BEHAVIOR ANALYSIS AMONG THE NATURAL SCIENCES. M. Jackson Marr School of Psychology Georgia Tech Atlanta, GA 30332-0170 USA mm27@prism.gatech.edu. LECTURE TOPICS. BEHAVIOR ANALYSIS AS A NATURAL SCIENCE CONTINGENCY: THE FUNDAMENTAL EXPLANATORY CONCEPT

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THE NATURAL SELECTION: BEHAVIOR ANALYSIS AMONG THE NATURAL SCIENCES

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  1. THE NATURAL SELECTION:BEHAVIOR ANALYSIS AMONG THE NATURAL SCIENCES M. Jackson Marr School of Psychology Georgia Tech Atlanta, GA 30332-0170 USA mm27@prism.gatech.edu

  2. LECTURE TOPICS • BEHAVIOR ANALYSIS AS A NATURAL SCIENCE • CONTINGENCY: THE FUNDAMENTAL • EXPLANATORY CONCEPT • 3. DIMENSION IN ACTION: THE PROBLEM • OF BEHAVIORAL UNITS • 4. MODELS IN BEHAVIOR ANALYSIS • 5. REDUCTION AND BEHAVIOR ANALYSIS

  3. In understanding behavior analysis as a natural science, we need to examine ties, conceptual and otherwise, between behavior analysis and other natural sciences—this is my overall theme.

  4. WHAT IS A NATURAL SCIENCE?

  5. SOME ISSUES TO CONSIDER • *1. What is a Natural Science? • *2. Ontology, Epistemology, and Patterns of Explanation. • *3. Behavior Analysis as a Biological Science *4. Contingency: The Fundamental Explanatory Concept • *5. The Problem of Behavioral Units • 6. The Role of Symmetry • 7. Dynamical Systems • *8. Mathematical Models • *9. Problems of Reductionism • 10. Scientific and Mathematical Verbal Behavior • 11. Creativity in the Sciences and Mathematics • *I plan to discuss these, given time.

  6. ONTOLOGY, EPISTEMOLOGY, AND PATTERNS OF EXPLANATION: Realism vs. Pragmatism, and Contextualism vs. Mechanism

  7. ELEMENTS OF CONTEXTUALISM • 1. The ongoing act in context as the unit of analysis. • 2. Focus on the whole event. • 3. Sensitivity to the role of context in understanding the event. • 4. “Successful working” as a pragmatic truth criterion.

  8. WHAT KINDS OF MECHANISMS: BEHAVIOR ANALYSIS AS A BIOLOGICAL SCIENCE

  9. GENERAL FUND OF BIOLOGICAL EXPLANATION • 1. Molecular (biochemical, biophysical) • 2. Cellular functions • 3. Tissue/organ functions • 4. Morphogenic/developmental • 5. Behavioral/environmental • 6. Species adaptation/evolution

  10. SOME SOURCES OF BIOLOGICAL VARIATION • MEIOSIS PROCESSES (e.g., recombination, linkage distance) • SEGREGATION (e.g., independent assortment, dominance, incomplete dominance, epistasis, pleiotropy) • NON-MENDELIAN PROCESSES (e.g., cytoplasmic inheritance, dependent assortment) • CHROMOSOMAL VARIATIONS (e.g., polyploidy, deletions, duplications, inversions, translocations) • MUTATIONS (e.g., transitions, transversions, tautometric, regulatory effects) • ALTERNATIVE SPLICING • QUANTITATIVE (e.g., polygenic expression, genetic drift, gene-environment interaction)

  11. MORE SOURCES OF BIOLOGICAL VARIATION • DEVELOPMENTAL DYNAMICS (e.g., “evo-devo”) • ALLOPATRIC, PARAPATRIC, AND SYMPATRIC ISOLATION • IN UTERO HISTORY • STOCHASTIC / CHAOTIC PHYSIOLOGICAL PROCESSES

  12. SOME SOURCES OF BEHAVIORAL VARIATION • 1. REFLEX PATTERNS AND THRESHOLDS • 2. SPECIES-SPECIFIC SENSORY / MOTOR PROGRAMS • 3. CONTINGENCIES AND DIFFERENTIAL SENSITIVITY TO THEM • 4. SHAPING: VARIATION AS A RESPONSE CLASS • 5. SELF-ORGANIZATION PROCESSES: “EMERGENCE” • 6. SOCIAL/CULTURAL DYNAMICS • 7. A HOST OF INDIVIDUAL DIFFERENCES RELATED TO ALL THE ABOVE AND MORE

  13. COMPUTATIONAL MODELING • NEURAL NETWORKS • CELLULAR AUTOMATA • DYNAMIC PROGRAMING • DYNAMIC STATE VARIABLE MODELS • GENETIC ALGORITHMS • SIMULATED ANNEALING • MONTE CARLO METHODS • STATISTICAL MECHANICS OF LEARNING

  14. PHYSICS Deterministic Reductive Mechanistic +Immediate Causation SIMPLICITY BIOLOGY Stochastic Emergent Selectionistic +Historical Causation COMPLEXITY PHYSICS VS. BIOLOGY/BEHAVIOR Mayr’s Distinctions

  15. CODA: So What?

  16. CONSEQUENCE-DRIVEN SYSTEMS Stevo Bozinovski (1995) REINFORCEMENT LEARNING R.S. Sutton & A.G. Barto (1998)

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